Migration is a common and essential technique for processing shot gathers of seismic data acquired in the field to produce a subsurface image with reflectors at their correct locations. There are various migration methods to produce a subsurface image such as Kirchhoff migration, beam migration, one-way wave equation migration, and reverse time migration. The basic principle of migration is almost the same regardless of the migration methods: (1) calculate forward wavefield from a source location using an assumed model of subsurface acoustic velocity, (2) reconstruct the receiver wavefield from receiver locations, and then (3) apply an imaging condition using the forward and reconstructed wavefields at image points. This principle migrates seismic events to their correct locations, but it also causes migration artifacts (known as migration swing or smile) because of the limited source and receiver coverage in a shot gather. The process of stacking (summing) migrated images of many shots, if they are regularly distributed in an area, cancels out the migration artifacts effectively. In a region of complex geologic structure like subsalt, stacking the shot images is not enough to cancel out the migration artifacts because of spatially varying illumination caused by complex overburdens and structures. For such regions, a more careful post-processing method may be applied to remove noise and enhance signals.
Migration methods may be categorized into ray-based and wave-equation-based migrations. Kirchhoff and beam migration are ray-based methods, and the inputs are usually surface offset (distance between source and receiver) gathers or angle gathers, and the migration output could be the subsurface images contributed by the different surface offsets or reflection angles at image points. Muting as a function of offset vs. depth or angle vs. depth can easily be applied to remove noises at shallow depths, and residual move-out can be applied to flatten reflectors with respect to offset or angle to enhance signals. These two post-processing techniques are very straightforward to apply to the migration output from Kirchhoff and beam migration because the outputs from the two methods are decomposed already in offset or angle at every image point.
Wave equation based migration has been widely applied in the oil and gas industry because it gives high fidelity images for geologically complex subsurface regions. The input for wave equation based migration is typically a shot gather, which contains traces at receiver points of various offsets, and it is typically performed shot-by-shot. The migration output is the subsurface image of each shot gather, and the final image is the stack of images of individual shots. Because of its nature of shot-by-shot implementation, it is difficult to apply two popular post-processing methods (muting and residual move-out) to wave equation based migrations such as one-way wave equation migration and reverse time migration. These two easy and effective methods could be applied to shot-based migration if angle gathers at each image point of each shot could be generated. However, the calculation of the angle gathers at each image point for each shot gather often is very computationally expensive.
An alternative way of post processing for shot-based migration is to make partial stacks from individual shot images, typically numbering between 9 and 20 and, usually based on the relative position of the source location to the image point (Whiteside et al. (2012), Compton and Stork (2012), Matson et al. (2012), and Vyas and Sharma (2012)).
Whiteside et al. (2012) proposed a Directional Imaging Stack (DIS) method for shot based migration, consisting of 1) making partial images (typically 3×3 or 4×5 grid and dimension of 2 to 3 km on a side), 2) calculating spatially dependent weights for the partial images in the previous step (by least squares to maximize the signal to noise ratio using a target image or by a semblance field), and 3) stacking the partial images with optimal weights. Compton and Stork (2012) proposed correlation based stacking for shot based migration, consisting of 1) stacking the prestack gathers to produce a reference model, 2)measuring the coherency of every sample in each prestack gather by correlation with the obtained reference model in step 1), 3) computing weights, and 4) restacking the weighted gathers. Matson et al. (2012) proposed Diversity Shot Stacking for Reverse Time Migration (DeSSeRT), consisting of 1) making sub-stacks of shot-image gathers according to relative location of image points from a shot location, 2) finding weights for the sub-stacks using a least-squares matching filter, and 3) applying the designed matching to the sub-stacks and stacking. Vyas and Sharma proposed a method of optimal stacking, consisting of 1) preconditioning the data, 2) finding a metric of similarity between multiple volumes of data of image, 3) searching for elements that are similar to each other, and 4) creating a stack using the identified chain.
The four methods mentioned above have demonstrated signal enhancement and noise removal for shot-based migration. However, these methods reduce the amount of input data for stacking substantially by the forming of partial or sub-stacks, where valuable information in individual images could be rendered invisible. In particular, local dips that may be separated on a shot-by-shot basis may be combined by partial stacking, reducing the effectiveness of dip-estimation methods in the area. The aforementioned remedial methods also need to have a target or reference image to compare a sub-stack with. Thus, the final output of these four methods is highly dependent on the quality of the target image. Amplitude information might not be preserved very well during the final stacking process with the different weights of each partial stack.
To overcome these disadvantages of the methods mentioned above, the present invention is designed to utilize all possible data for the stacking process, to be an automated process only lightly guided by geologically interpreted surfaces, and to be iterative so as to extract missing events from the previous iteration.